coef.glmgraph {glmgraph} | R Documentation |
Retrieve coefficients from a fitted "glmgraph" object, depending on the user-specified regularization parameters.
## S3 method for class 'glmgraph' coef(object,lambda1,lambda2,...)
object |
Fitted |
lambda1 |
Values of the regularization parameter |
lambda2 |
The user-specified regularization |
... |
Other parameters to |
If lambda1
and lambda2
are missing, all coefficients of fitted glmgraph
object will be returned.
If only lambda1
is missing, then coefficients of specified lambda2
will be returned.
The object returned depends on type.
Li Chen <li.chen@emory.edu>, Jun Chen <chen.jun2@mayo.edu>
Li Chen. Han Liu. Hongzhe Li. Jun Chen. (2015) glmgraph: Graph-constrained Regularization for Sparse Generalized Linear Models.(Working paper)
predict.glmgraph
,glmgraph
set.seed(1234) library(glmgraph) n <- 100 p1 <- 10 p2 <- 90 p <- p1+p2 X <- matrix(rnorm(n*p), n,p) magnitude <- 1 ## construct laplacian matrix from adjacency matrix A <- matrix(rep(0,p*p),p,p) A[1:p1,1:p1] <- 1 A[(p1+1):p,(p1+1):p] <- 1 diag(A) <- 0 btrue <- c(rep(magnitude,p1),rep(0,p2)) intercept <- 0 eta <- intercept+X%*%btrue diagL <- apply(A,1,sum) L <- -A diag(L) <- diagL ### gaussian Y <- eta+rnorm(n) obj <- glmgraph(X,Y,L) coefs <- coef(obj) coefs <- coef(obj,lambda2=0.01) coefs <- coef(obj,lambda1=c(0.11,0.12)) coefs <- coef(obj,lambda1=c(0.11,0.12),lambda2=0.01)